//- 💫 DOCS > USAGE > SPACY 101 include ../../_includes/_mixins +h(2, "whats-spacy") What's spaCy? +grid +grid-col("half") +grid-col("half") +infobox +label.o-block-small Table of contents +list("numbers").u-text-small.o-no-block +item #[+a("#features") Features] +item #[+a("#annotations") Linguistic annotations] +item #[+a("#annotations-token") Tokenization] +item #[+a("#annotations-pos-deps") POS tags and dependencies] +item #[+a("#annotations-ner") Named entities] +item #[+a("#vectors-similarity") Word vectos and similarity] +item #[+a("#pipelines") Pipelines] +item #[+a("#vocab") Vocab, hashes and lexemes] +item #[+a("#serialization") Serialization] +item #[+a("#training") Training] +item #[+a("#architecture") Architecture] +item #[+a("#community") Community & FAQ] +h(2, "features") Features p | Across the documentations, you'll come across mentions of spaCy's | features and capabilities. Some of them refer to linguistic concepts, | while others are related to more general machine learning functionality. +aside | If one of spaCy's functionalities #[strong needs a model], it means that | you need to have one our the available | #[+a("/docs/usage/models") statistical models] installed. Models are used | to #[strong predict] linguistic annotations – for example, if a word is | a verb or a noun. +table(["Name", "Description", "Needs model"]) +row +cell #[strong Tokenization] +cell Segmenting text into words, punctuations marks etc. +cell #[+procon("con")] +row +cell #[strong Part-of-speech] (POS) #[strong Tagging] +cell Assigning word types to tokens, like verb or noun. +cell #[+procon("pro")] +row +cell #[strong Dependency Parsing] +cell | Assigning syntactic dependency labels, i.e. the relations between | individual tokens. +cell #[+procon("pro")] +row +cell #[strong Sentence Boundary Detection] (SBD) +cell Finding and segmenting individual sentences. +cell #[+procon("pro")] +row +cell #[strong Named Entity Recongition] (NER) +cell | Labelling named "real-world" objects, like persons, companies or | locations. +cell #[+procon("pro")] +row +cell #[strong Rule-based Matching] +cell | Finding sequences of tokens based on their texts and linguistic | annotations, similar to regular expressions. +cell #[+procon("con")] +row +cell #[strong Similarity] +cell | Comparing words, text spans and documents and how similar they | are to each other. +cell #[+procon("pro")] +row +cell #[strong Training] +cell Updating and improving a statistical model's predictions. +cell #[+procon("neutral")] +row +cell #[strong Serialization] +cell Saving objects to files or byte strings. +cell #[+procon("neutral")] +h(2, "annotations") Linguistic annotations p | spaCy provides a variety of linguistic annotations to give you | #[strong insights into a text's grammatical structure]. This includes the | word types, like the parts of speech, and how the words are related to | each other. For example, if you're analysing text, it makes a huge | difference whether a noun is the subject of a sentence, or the object – | or whether "google" is used as a verb, or refers to the website or | company in a specific context. p | Once you've downloaded and installed a #[+a("/docs/usage/models") model], | you can load it via #[+api("spacy#load") #[code spacy.load()]]. This will | return a #[code Language] object contaning all components and data needed | to process text. We usually call it #[code nlp]. Calling the #[code nlp] | object on a string of text will return a processed #[code Doc]: +code. import spacy nlp = spacy.load('en') doc = nlp(u'Apple is looking at buying U.K. startup for $1 billion') p | Even though a #[code Doc] is processed – e.g. split into individual words | and annotated – it still holds #[strong all information of the original text], | like whitespace characters. This way, you'll never lose any information | when processing text with spaCy. +h(3, "annotations-token") Tokenization include _spacy-101/_tokenization +infobox | To learn more about how spaCy's tokenization rules work in detail, | how to #[strong customise and replace] the default tokenizer and how to | #[strong add language-specific data], see the usage guides on | #[+a("/docs/usage/adding-languages") adding languages] and | #[+a("/docs/usage/customizing-tokenizer") customising the tokenizer]. +h(3, "annotations-pos-deps") Part-of-speech tags and dependencies +tag-model("dependency parse") include _spacy-101/_pos-deps +infobox | To learn more about #[strong part-of-speech tagging] and rule-based | morphology, and how to #[strong navigate and use the parse tree] | effectively, see the usage guides on | #[+a("/docs/usage/pos-tagging") part-of-speech tagging] and | #[+a("/docs/usage/dependency-parse") using the dependency parse]. +h(3, "annotations-ner") Named Entities +tag-model("named entities") include _spacy-101/_named-entities +infobox | To learn more about entity recognition in spaCy, how to | #[strong add your own entities] to a document and how to | #[strong train and update] the entity predictions of a model, see the | usage guides on | #[+a("/docs/usage/entity-recognition") named entity recognition] and | #[+a("/docs/usage/training-ner") training the named entity recognizer]. +h(2, "vectors-similarity") Word vectors and similarity +tag-model("vectors") include _spacy-101/_similarity include _spacy-101/_word-vectors +infobox | To learn more about word vectors, how to #[strong customise them] and | how to load #[strong your own vectors] into spaCy, see the usage | guide on | #[+a("/docs/usage/word-vectors-similarities") using word vectors and semantic similarities]. +h(2, "pipelines") Pipelines include _spacy-101/_pipelines +infobox | To learn more about #[strong how processing pipelines work] in detail, | how to enable and disable their components, and how to | #[strong create your own], see the usage guide on | #[+a("/docs/usage/language-processing-pipeline") language processing pipelines]. +h(2, "vocab") Vocab, hashes and lexemes include _spacy-101/_vocab +h(2, "serialization") Serialization include _spacy-101/_serialization +infobox | To learn more about #[strong serialization] and how to | #[strong save and load your own models], see the usage guide on | #[+a("/docs/usage/saving-loading") saving, loading and data serialization]. +h(2, "training") Training include _spacy-101/_training +h(2, "architecture") Architecture +under-construction +image include ../../assets/img/docs/architecture.svg .u-text-right +button("/assets/img/docs/architecture.svg", false, "secondary").u-text-tag View large graphic +table(["Name", "Description"]) +row +cell #[+api("language") #[code Language]] +cell | A text-processing pipeline. Usually you'll load this once per | process as #[code nlp] and pass the instance around your application. +row +cell #[+api("doc") #[code Doc]] +cell A container for accessing linguistic annotations. +row +cell #[+api("span") #[code Span]] +cell A slice from a #[code Doc] object. +row +cell #[+api("token") #[code Token]] +cell | An individual token — i.e. a word, punctuation symbol, whitespace, | etc. +row +cell #[+api("lexeme") #[code Lexeme]] +cell | An entry in the vocabulary. It's a word type with no context, as | opposed to a word token. It therefore has no part-of-speech tag, | dependency parse etc. +row +cell #[+api("vocab") #[code Vocab]] +cell | A lookup table for the vocabulary that allows you to access | #[code Lexeme] objects. +row +cell #[code Morphology] +cell | Assign linguistic features like lemmas, noun case, verb tense etc. | based on the word and its part-of-speech tag. +row +cell #[+api("stringstore") #[code StringStore]] +cell Map strings to and from hash values. +row +row +cell #[+api("tokenizer") #[code Tokenizer]] +cell | Segment text, and create #[code Doc] objects with the discovered | segment boundaries. +row +cell #[+api("matcher") #[code Matcher]] +cell | Match sequences of tokens, based on pattern rules, similar to | regular expressions. +h(3, "architecture-pipeline") Pipeline components +table(["Name", "Description"]) +row +cell #[+api("tagger") #[code Tagger]] +cell Annotate part-of-speech tags on #[code Doc] objects. +row +cell #[+api("dependencyparser") #[code DependencyParser]] +cell Annotate syntactic dependencies on #[code Doc] objects. +row +cell #[+api("entityrecognizer") #[code EntityRecognizer]] +cell | Annotate named entities, e.g. persons or products, on #[code Doc] | objects. +h(3, "architecture-other") Other classes +table(["Name", "Description"]) +row +cell #[+api("binder") #[code Binder]] +cell Container class for serializing collections of #[code Doc] objects. +row +cell #[+api("goldparse") #[code GoldParse]] +cell Collection for training annotations. +row +cell #[+api("goldcorpus") #[code GoldCorpus]] +cell | An annotated corpus, using the JSON file format. Manages | annotations for tagging, dependency parsing and NER. +h(2, "community") Community & FAQ p | We're very happy to see the spaCy community grow and include a mix of | people from all kinds of different backgrounds – computational | linguistics, data science, deep learning and research. If you'd like to | get involved, below are some answers to the most important questions and | resources for further reading. +h(3, "faq-help-code") Help, my code isn't working! p | Bugs suck, and we're doing our best to continuously improve the tests | and fix bugs as soon as possible. Before you submit an issue, do a | quick search and check if the problem has already been reported. If | you're having installation or loading problems, make sure to also check | out the #[+a("/docs/usage#troubleshooting") troubleshooting guide]. Help | with spaCy is available via the following platforms: +aside("How do I know if something is a bug?") | Of course, it's always hard to know for sure, so don't worry – we're not | going to be mad if a bug report turns out to be a typo in your | code. As a simple rule, any C-level error without a Python traceback, | like a #[strong segmentation fault] or #[strong memory error], | is #[strong always] a spaCy bug.#[br]#[br] | Because models are statistical, their performance will never be | #[em perfect]. However, if you come across | #[strong patterns that might indicate an underlying issue], please do | file a report. Similarly, we also care about behaviours that | #[strong contradict our docs]. +table(["Platform", "Purpose"]) +row +cell #[+a("https://stackoverflow.com/questions/tagged/spacy") StackOverflow] +cell | #[strong Usage questions] and everything related to problems with | your specific code. The StackOverflow community is much larger | than ours, so if your problem can be solved by others, you'll | receive help much quicker. +row +cell #[+a("https://gitter.im/" + SOCIAL.gitter) Gitter chat] +cell | #[strong General discussion] about spaCy, meeting other community | members and exchanging #[strong tips, tricks and best practices]. | If we're working on experimental models and features, we usually | share them on Gitter first. +row +cell #[+a(gh("spaCy") + "/issues") GitHub issue tracker] +cell | #[strong Bug reports] and #[strong improvement suggestions], i.e. | everything that's likely spaCy's fault. This also includes | problems with the models beyond statistical imprecisions, like | patterns that point to a bug. +infobox | Please understand that we won't be able to provide individual support via | email. We also believe that help is much more valuable if it's shared | publicly, so that #[strong more people can benefit from it]. If you come | across an issue and you think you might be able to help, consider posting | a quick update with your solution. No matter how simple, it can easily | save someone a lot of time and headache – and the next time you need help, | they might repay the favour. +h(3, "faq-contributing") How can I contribute to spaCy? p | You don't have to be an NLP expert or Python pro to contribute, and we're | happy to help you get started. If you're new to spaCy, a good place to | start is the | #[+a(gh("spaCy") + '/issues?q=is%3Aissue+is%3Aopen+label%3A"help+wanted+%28easy%29"') #[code help wanted (easy)] label] | on GitHub, which we use to tag bugs and feature requests that are easy | and self-contained. We also appreciate contributions to the docs – whether | it's fixing a typo, improving an example or adding additional explanations. p | Another way of getting involved is to help us improve the | #[+a("/docs/usage/adding-languages#language-data") language data] – | especially if you happen to speak one of the languages currently in | #[+a("/docs/api/language-models#alpha-support") alpha support]. Even | adding simple tokenizer exceptions, stop words or lemmatizer data | can make a big difference. It will also make it easier for us to provide | a statistical model for the language in the future. Submitting a test | that documents a bug or performance issue, or covers functionality that's | especially important for your application is also very helpful. This way, | you'll also make sure we never accidentally introduce regressions to the | parts of the library that you care about the most. p strong | For more details on the types of contributions we're looking for, the | code conventions and other useful tips, make sure to check out the | #[+a(gh("spaCy", "CONTRIBUTING.md")) contributing guidelines]. +infobox("Code of Conduct") | spaCy adheres to the | #[+a("http://contributor-covenant.org/version/1/4/") Contributor Covenant Code of Conduct]. | By participating, you are expected to uphold this code. +h(3, "faq-project-with-spacy") | I've built something cool with spaCy – how can I get the word out? p | First, congrats – we'd love to check it out! When you share your | project on Twitter, don't forget to tag | #[+a("https://twitter.com/" + SOCIAL.twitter) @#{SOCIAL.twitter}] so we | don't miss it. If you think your project would be a good fit for the | #[+a("/docs/usage/showcase") showcase], #[strong feel free to submit it!] | Tutorials are also incredibly valuable to other users and a great way to | get exposure. So we strongly encourage #[strong writing up your experiences], | or sharing your code and some tips and tricks on your blog. Since our | website is open-source, you can add your project or tutorial by making a | pull request on GitHub. +aside("Contributing to spacy.io") | All showcase and tutorial links are stored in a | #[+a(gh("spaCy", "website/docs/usage/_data.json")) JSON file], so you | won't even have to edit any markup. For more info on how to submit | your project, see the | #[+a(gh("spaCy", "CONTRIBUTING.md#submitting-a-project-to-the-showcase")) contributing guidelines] | and our #[+a(gh("spaCy", "website")) website docs]. p | If you would like to use the spaCy logo on your site, please get in touch | and ask us first. However, if you want to show support and tell others | that your project is using spaCy, you can grab one of our | #[strong spaCy badges] here: - SPACY_BADGES = ["built%20with-spaCy-09a3d5.svg", "made%20with%20❤%20and-spaCy-09a3d5.svg", "spaCy-v2-09a3d5.svg"] +quickstart([{id: "badge", input_style: "check", options: SPACY_BADGES.map(function(badge, i) { return {id: i, title: "", checked: (i == 0) ? true : false}}) }], false, false, true) .c-code-block(data-qs-results) for badge, i in SPACY_BADGES - var url = "https://img.shields.io/badge/" + badge +code(false, "text", "star").o-no-block(data-qs-badge=i)=url +code(false, "text", "code").o-no-block(data-qs-badge=i). <a href="#{SITE_URL}"><img src="#{url}" height="20"></a> +code(false, "text", "markdown").o-no-block(data-qs-badge=i). [![spaCy](#{url})](#{SITE_URL})